Video Representation: Initial attempts to encode visual content of a video are broadly formulated with hand-crafted features. These features are extracted from either local patches [1]}, [2]} and/or local trajectories [3]} by exploiting regional gradient features. Later, they are aggregated over frames with various pooling techniques to obtain a single compact representation [4]}, [5]}. Recently, feature extraction and aggregation steps are adapted to learnable modules. Frame-level [6]}, [7]}, spatio-temporal-level [8]}, [9]} and motion-level [10]} convolutions are extensively studied to extract robust features from videos. Moreover, trainable pooling techniques are introduced  [11]}, [12]} to boost discriminative representation learning rather than unsupervised feature-space clustering [4]}, [5]}.
